Myopia progression patterns among paediatric patients in a clinical setting

Abstract

PurposeThis retrospective analysis of electronic medical record (EMR) data investigated the natural history of myopic progression in children from optometric practices in Ireland.MethodsThe analysis was of myopic patients aged 7–17 with multiple visits and not prescribed myopia control treatment. Sex‐ and age‐specific population centiles for annual myopic progression were derived by fitting a weighted cubic spline to empirical quantiles. These were compared to progression rates derived from control group data obtained from 17 randomised clinical trials (RCTs) for myopia. Linear mixed models (LMMs) were used to allow comparison of myopia progression rates against outputs from a predictive online calculator. Survival analysis was performed to determine the intervals at which a significant level of myopic progression was predicted to occur.ResultsMyopia progression was highest in children aged 7 years (median: −0.67 D/year) and progressively slowed with increasing age (median: −0.18 D/year at age 17). Female sex (p < 0.001), a more myopic SER at baseline (p < 0.001) and younger age (p < 0.001) were all found to be predictive of faster myopic progression. Every RCT exhibited a mean progression higher than the median centile observed in the EMR data, while clinic‐based studies more closely matched the median progression rates. The LMM predicted faster myopia progression for patients with higher baseline myopia levels, in keeping with previous studies, which was in contrast to an online calculator that predicted slower myopia progression for patients with higher baseline myopia. Survival analysis indicated that at a recall period of 12 months, myopia will have progressed in between 10% and 70% of children, depending upon age.ConclusionsThis study produced progression centiles of untreated myopic children, helping to define the natural history of untreated myopia. This will enable clinicians to better predict both refractive outcomes without treatment and monitor treatment efficacy, particularly in the absence of axial length data.

Associations between Ocular Biometry, Refractive Error, and Body Characteristics

“…Studying possible disparities based on factors such as sex or height may be useful in bringing a new approach to these calculations and providing a more complete and accurate result. Similarly, understanding the relationship between these parameters may be valuable for optimizing the equations underlying the myopia progression prediction calculators [21].…”

Combination effect of optical defocus and low dose atropine in myopia control: Study protocol for a randomized clinical trial

“…Despite these interventions, myopia progression remains uncurbed in some cases, particularly in younger children [ 13 ]. Hence, there is a pressing need for more effective treatments that could potentially stop myopia progression entirely and prevent the onset of high myopia.…”